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Record W2517605366 · doi:10.4309/jgi.2016.33.10

Cognitive Behavioural Group Therapy for Problem Gamblers who Gamble over the Internet: A Controlled Study

2016· article· en· W2517605366 on OpenAlexvenueno aff
Nicholas Harris, Dwight Mazmanian

Bibliographic record

VenueJournal of Gambling Issues · 2016
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionPsychologyThe InternetTreatment and control groupsClinical psychologyTest (biology)PerceptionCognitionPsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Several studies have found higher rates of problem gambling among Internet gamblers than non-Internet gamblers. Because of easy access and convenience, along with other gaming characteristics, many researchers in the field have advanced the argument that Internet gambling is potentially more addictive and problematic than land-based gambling activities. However, research examining the efficacy of treatments for problem gamblers who gamble over the Internet has not yet been conducted. The purpose of the present study was to examine the efficacy of group cognitive behavioural therapy for self-identified problem Internet gamblers. Thirty-two participants were randomly assigned to either the treatment group (n = 16) or wait list (delayed treatment) comparison group (n = 16). Results indicated that the treatment was efficacious in improving three of the four dependent variables from pre- to post-test/treatment: number of DSM-IV criteria for pathological gambling endorsed, perception of control over gambling, and number of sessions gambled. No significant pre- to post-test/treatment difference was found between groups on desire to gamble. Groups were combined to examine treatment outcome over time, with results showing significant pre- to post-treatment and pre- to three-month post-treatment improvement for all four dependent variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.232
GPT teacher head0.452
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2016
Admission routes1
Has abstractyes

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